PERBANDINGAN KLASIFIKASI NASABAH KREDIT MENGGUNAKAN REGRESI LOGISTIK BINER DAN CART (CLASSIFICATION AND REGRESSION TREES)

*Agung Waluyo -  Jurusan Statistika, FSM, Universitas Diponegoro, Indonesia
Moch. Abdul Mukid -  Jurusan Statistika, FSM, Universitas Diponegoro, Indonesia
Triastuti Wuryandari -  Jurusan Statistika, FSM, Universitas Diponegoro, Indonesia
Published: 28 Dec 2014.
Open Access
Citation Format:
Article Info
Section: Articles
Language: EN
Full Text:
Statistics: 392 410
Abstract

Credit is the greatest asset managed the bank and also the most dominant contributor to the bank’s revenue. Debtors to pay their credit to the bank may smoothly or jammed. This study aims to identify the variables that affect a debtor’s credit status and compare the acuration of classification method both classification and regression trees (CART)  and logistic regression. The variables used were debtor’s gender, education level, occupation, marital status, and income. By using logistic regression, it was known that only the debtor’s income effect their credit status with the classification accuration reach into 80%. By using CART, there were some variables affect the credit status and the classification accuration 80,9%. This paper showed that the performance of CART in classifying the credit status of debtors was better than logistic regression.

 

Keywords: Credit Status, Logistic Regression, CART

 

Article Metrics: